JOURNAL OF REGIONAL SCIENCE, VOL. 50, NO. 1, 2010, pp. 423–448
FEEDBACK BETWEEN REAL ESTATE AND URBAN
ECONOMICS∗
Todd Sinai
The Wharton School, University of Pennsylvania, 1465 Steinberg Hall-Dietrich Hall,
3620 Locust Walk, Philadelphia, PA 19104-6302.
E-mail: sinai@wharton.upenn.edu
ABSTRACT. This paper considers the implications of increasing land supply constraints
in the United States on urban demand. First, because shifts in demand are now capitalized more into the price of land, house prices in some metropolitan areas have grown
increasingly unaffordable to typical households. This might have an effect on the fundamental character of such cities. Second, the effect of home owners’ financial interests as
landowners on their decisions about what regulations or investments in their communities to support may become stronger. Third, researchers may now be able to better use
land prices to make inferences about urban demand. However, interpreting real estate
prices still is tricky.
Characterizing the intersection of real estate finance and economics and
urban economics turns out to be a not particularly straightforward thing. That
perhaps should not come as a surprise, since neither field has a tidy definition,
all wrapped up with crisp edges and neat corners, plus a bow for good measure.
Instead, urban economics is broadly concerned with the study of cities and
real estate is broadly concerned with the study of commercial and residential
property. Since most of the world’s real estate value is in cities, it is natural
that the fields overlap considerably and somewhat surprising that they are not
more fully integrated.
Indeed, those who study real estate and those who study urban economics
are interested in many of the same phenomena. For example: patterns of
agglomeration—with households choosing to live in close proximity to other
households, or firms choosing to locate their operations near other firms—and
how that agglomeration might evolve over time. Urban growth—and where it
might occur, and by how much. Incomes—and how they differ across space.
However, those who have focused on real estate and those who have focused
on urban economics often have come at these topics from disparate points
of view. To real estate practitioners real estate is an input to some broader
goal. To some, real estate is an investment, an asset that one acquires in
∗
This paper was prepared for the Symposium in honor of the 50th Anniversary of the Journal
of Regional Science. I am grateful to the Research Sponsors Program of the Zell-Lurie Real Estate
Center at Wharton for research support and to Gilles Duranton, Joseph Gyourko, and Grace Wong
for their helpful comments.
Received: April 2009; revised: September 2009; accepted September 2009.
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DOI: 10.1111/j.1467-9787.2009.00660.x
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order to produce a financial return. For their calculus, what matters is real
estate’s income earning potential—its rent—and its current and future prices.
To others, real estate is a factor of production, akin to capital or labor, necessary
for the operation of an enterprise. They tend to focus on the cost of obtaining
real estate. By contrast, to urban economists, real estate is an output of a more
general underlying process. The same forces that lead to the formation of cities
make particular locations valuable.
These two viewpoints intersect via the market for land. One can think of
urban economics as being in part about what affects the demand for particular
locations. An agglomeration economy, where a firm’s productivity is determined
in part by its proximity to other firms, is one example of such a demand shifter.
Firms would have greater demand to locate near existing firms if there were
positive spillovers to doing so. City amenities, such as weather or art museums,
are other examples. When locations with such amenities are scarce, land commands rents. On the real estate side, the financial market transforms current
and expected future rents into asset prices for the locations.
Because of this interconnectedness between real estate and urban economics, it is interesting to think of the implications of urban economics for
real estate and vice versa. In this paper, I emphasize the role of land prices
and ponder how rising prices could reinforce or deter the demand for certain
locations that led to the price growth in the first place. I begin by discussing
a necessary condition for growth in the price of land: Inelastic supply. Because
supply constraints in the United States have increased, shifts in demand may
be more capitalized into the price of land than in the past. This has several
interesting implications. First, the price of real estate, relative to the national
income distribution, has increased in some metropolitan areas over the very
long run. For example, houses in some metropolitan areas, especially those on
the west coast of the United States, have slowly grown unaffordable to all but
the highest-income decile or so of the national income distribution. I then discuss what this phenomenon might imply for the future of cities, given existing
hypotheses about the determinants of urban growth. Second, when residents
own real estate in an area where land is inelastically supplied, their decisions
about what regulations or investments in their communities to support may
be affected by their financial interests in obtaining higher values for their locations. Third, real estate prices may help researchers make inferences about
urban demand. However, using them is tricky. Prices convey information, but
the signal is somewhat noisy. For one, the extent of price capitalization is determined by the elasticity of supply of land, which has been changing over time
and across cities in the United States. In addition, while real estate rents are
determined by the supply and demand for space, we typically observe only real
estate prices. Changes in those prices result not only from supply and demand
fundamentals, but from other factors that influence the asset market, such as
interest rates and the expectations of future growth.
The rest of this paper proceeds as follows: I first present how urban economics can be thought of as demand for a location and how the elasticity of land
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supply translates the demand into land prices. The evolution of the land supply
elasticity over time is described. Next, I explore metropolitan area house prices
between 1950 and 2000 and compare them to the national income distribution.
I outline some potential implications of rapidly growing house prices. In Section
3, I discuss why it might matter if households are real estate owners as well
as users. Section 4 considers the role of asset market equilibrium in setting
real estate prices and the difficulties in using them for inferences in empirical
work. Lastly, Section 5 concludes.
1. USING REAL ESTATE RENTS AS A MEASURE OF URBAN DEMAND
A large portion of urban economics is concerned with the demand for urban
location: Why is it that both firms and households locate in close proximity to
other firms and households in cities? There are a number of potential reasons
and, in all likelihood, all have held to some degree or at some point in time.
Firms might wish to locate near other firms because of returns to agglomeration, whether through lower-cost transportation of goods and services between
them, knowledge spillovers amongst them, or by having access to a thick pool of
labor that accumulates around a large concentration of employers. Households
might wish to live near other households because they enjoy them, or because
it takes a sufficiently large concentration of residents to make it profitable for
firms to provide locally the goods and services, and governments to provide
public amenities, that the residents want. Once those amenities are in place,
the location would be more valuable to the marginal household.
One can search for evidence of these factors along a number of dimensions.
A large strand of the literature tests whether firms are more productive when
they have agglomerated by looking at measures of output. Another strand concentrates on the implication for workers: if cities are more productive, workers
should receive higher wages. Other research considers firm locations. And yet
another set of papers examine city growth: If there is a benefit to agglomeration, cities should expand in population and the number of firms, at least
until the city becomes so large that it becomes too congested, unwieldy, and
inefficient to administer.1
However, if there is a benefit to a particular location—for whatever
reason—the value may be in part capitalized into the price of the land. Demand for a type of location can manifest itself in two ways: in higher land rents
and in greater supply. How much of the benefit is incorporated into the land
rent depends upon the elasticity of supply of land that is substitutable for that
particular location. (One unique location, of course, is perfectly inelastic, except
to the degree that more uses can be put onto the same parcel by increasing the
density.) If land supply is very elastic, little of the benefit will be reflected in the
1
This literature is too voluminous to properly reference in this article. See Rosenthal and
Strange (2004), Moretti (2004) and Strange (2005) for overviews, as well as the papers by Bacolod,
Blum, and Strange (2010), Glaeser and Resseger (2010), and Puga (2010).
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r
S’
S
A
B
C
D’
D
Q
FIGURE 1: Supply and Demand for Real Estate.
land rent as long as property prices exceed the cost of construction. Instead, if
property values rise above construction costs, developers will simply add more
“city” to the existing stock until prices (and rents) fall to the level where it is
no longer profitable to build. By contrast, if land supply is very inelastic, the
city cannot expand, developers cannot build, and the benefits of location are
capitalized into land rents.
That static process can be seen in a basic demand-supply framework, as
in Figure 1, which plots demand and supply curves in rent-quantity space.
An inelastic supply curve for urban locations is depicted with the S supply
curve whereas the S supply curve is more elastic. Where the demand curve
for urban locations, D, intersects the supply curve is the clearing price, where
all urban space is just filled. That price, C, is the rent, or the periodic cost of
using a particular space. Suppose the demand curve for urban locations shifts
out, to D . If land is inelastically supplied, an increase in demand is capitalized
fully into price, as rents rise from C to A. If land is more elastically supplied,
the same increase in demand leads to a smaller increase in rents (from C to
B).2
The degree of capitalization into land rents is important for empirical
research in real estate or urban economics because it affects where and how
much the symptoms of urban demand show up. Many common measures of
urban demand, such as population growth, are affected by the elasticity of
land supply. For example, consider an outward shift of the demand curve. That
2
In the short-run, the supply curve might be nearly vertical (line S ) as city boundaries are
pretty much set and development of new housing or commercial space in existing cities is a slow
process. Beyond the short-run, if the price of urban space is high enough, the market may create
more urban locations, leading the supply curve to be flatter than vertical (line S). One can imagine
that existing cities could either expand their boundaries or increase their density (by building more
space within the same boundaries). Or, new cities that substitute for existing cities could arise,
increasing the aggregate supply of urban locations.
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demand shift will be manifested in city growth and, eventually, a larger city
if supply is perfectly elastic. As an empirical researcher, one could thus infer
that demand for a city increased if the city grew. However, the same demand
shock would be reflected solely in rents, with no city growth at all, if supply
is perfectly inelastic. And, if the elasticity of supply is in between, some of the
greater demand would appear in city growth and some in rent.
The flip side of this particular coin is that land rents themselves, if used
carefully, could also provide information about the extent of urban demand.
Greater demand translates into higher rent, ceteris paribus. And since rents
are expressed in dollars, the increment in rent from higher demand, assuming
supply is inelastic, can be interpreted as the value of whatever factors are
inducing that higher demand.
A large set of empirical papers have used land prices to assign values to
some feature of the urban economy. This capitalization strategy can be applied
whenever the feature of value has a spatial component to its distribution. Papers that follow a Rosen (1979) and Roback (1982) view of spatial equilibrium,
which theorizes that agents must trade off wages, land rents, and local amenities when choosing between cities, such as Blomquist, Berger, and Hoehn (1988)
or Gyourko and Tracy (1989, 1991), have long found that differences in amenity
levels or public finance across jurisdictions are capitalized into land rents and
wages. Amenities, such as quality-of-life or efficient government, are frequently
tied to a geographical location or a political jurisdiction. Thus people should
be willing to pay a real estate premium or accept a lower wage to live in those
jurisdictions. For example, Black (1999) estimates the value of better schools
by seeing how much higher house prices are when the houses are located in the
catchment area of a school with better test scores.3 Researchers in environmental economics have long estimated the cost of some environmental negative—a
toxic waste dump, perhaps—by measuring how much less land is worth when
it is in closer proximity to it (Gallagher and Greenstone, 2008). Productivity,
too, can be capitalized into real estate values when location affects output.
Greenstone and Moretti (2004) estimate the value of productivity spillovers by
seeing how much land prices rise when a firm chooses to locate in a particular
area.4 If that firm provides positive externalities, the premium others would
be willing to pay to locate close to it should go up, leading to higher land values. Van Nieuwerburg and Weill (2007) examine the relationship between local
productivity shocks and the distribution of house prices.
3
For other capitalization studies, see: Oates (1969); Bayer, Ferreira, and McMillan (2007);
and Cellini, Ferreira, and Rothstein (2009), which concludes that investments in school facilities
have a positive value net of fiscal cost since constructing them yield increases in house values.
4
While most theories focus on land rents and prices, most empirical work considers properties, i.e. bundles of land and structures. Land isolates the value of location since how much to
spend on the structure atop the land is an independent choice. However, unimproved land is rarely
traded and what is, is not a representative sample. Thus, most empirical researchers use property
rents and prices. See Davis and Heathcote (2007) for an aggregate decomposition of properties into
structures and land rents.
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While land rents can convey significant information about the demand for
locations, interpreting that information can be difficult for at least two reasons.
First, true rent is often difficult to observe. While the theoretical rent is a spot
price, in practice rents that are paid are often specified in a multi-year contract
signed years earlier and which may not reflect current market conditions. In
addition, when it comes to housing, renter households tend to be quite different
than owner households and thus one cannot easily extrapolate from rent paid
by renters to the rent that would be paid by owners were they to pay rent (Smith
and Smith, 2006). Instead, we are much more likely to observe the price paid
by a purchaser of property. (We will discuss the particular issues that arise
when one observes prices rather than rents in empirical work in Section 4,
below.)
Second, most research that studies the effect of something on land rents or
prices assumes that land supply in an area or jurisdiction is perfectly inelastically supplied. Sometimes the assumption of a vertical supply curve is appropriate. But much of the time, it is not. Complicating the matter, the supply
elasticity varies not only across jurisdictions, but over time within jurisdictions,
and even can differ depending on the initial land value.
A series of recent papers have noted that an important component of why
house prices vary across metropolitan areas have to do with differences in the
elasticity of supply. The reasons for these cross-sectional differences in supply
elasticity are varied. Saiz (2008) shows that the topography of an area is one
factor. When much of a metropolitan area’s footprint is steeply sloped or under
water, it is more expensive to build new structures, leading to a lower elasticity of supply. Gyourko and Saiz (2006) point out that construction costs, too,
vary considerably across metropolitan areas. While geographic constraints on
construction do not change over time, how binding they are may vary considerably. As cities grow, eventually they may exhaust their easily developable land.
For example, the remaining land might be costly to develop or less desirable
leading to a less elastic supply of land. Or, land use regulation—limits on new
construction imposed by cities and towns—may have become more stringent,
as argued by Glaeser, Gyourko, and Saks (henceforth GGS) (2005a), Quigley
and Raphael (2005), and Glaeser and Ward (2006). For an example of one such
piece of evidence, GGS (2005b) estimates that the marginal value of building
an additional floor on a high-rise building in Manhattan is much higher than
the marginal cost of constructing the additional floor. The gap, they conclude,
must be due to regulatory barriers or nonconstruction costs, such as lawsuits
from opposed parties.
Circumstantial evidence of this kind of pattern is developed in Gyourko,
Mayer, and Sinai (2006), henceforth GMS. A number of metropolitan areas
in the United States appear to have become more supply constrained between
1950 and 2000, in that the rate of new home construction in those areas greatly
diminished and concurrently the rate of house price growth rose. The authors
take that to be evidence of a rotation of the supply curve in those metropolitan
areas from more-horizontal to more-vertical.
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TABLE 1: Changing Land Supply Elasticities Across MSAs, 1950–2000
Year
1950–1970
1960–1980
1970–1990
1980–2000
Average Slope of
the Supply Curve
Standard Deviation of the
Slope of the Supply Curve
0.77
0.85
0.87
0.99
0.58
0.46
1.23
1.79
Notes: Data are from the U.S. Decennial Census, 1950–2000. The “slope of the supply curve”
is defined as the ratio of the average house price growth over the prior 20 years to the average
housing unit growth, as in Gyourko et al. (2006). A separate slope is computed for each MSA in
each year from 1970 to 2000. A higher number corresponds to more inelastic supply since it implies
that a change in demand is reflected more in price changes than quantity changes.
Evidence of that same pattern can be seen in Table 1, which reports statistics on the inferred elasticity of supply, measured as the slope of the supply
curve, across MSAs over a number of decades. The “slope of the supply curve”
is defined as the ratio of the average house price growth over the prior 20
years to the average housing unit growth, as in GMS (2006). A separate slope
is computed for each MSA in each year from 1970 to 2000. A higher number
corresponds to more inelastic supply since it implies that a change in demand
is reflected more in price changes than quantity changes.
The average slope (across equally weighted MSAs) for each 20-year period
is reported in the second column of Table 1. Over time, the supply curve has
become somewhat more steeply upward-sloping, on average, ranging from 0.77
over the 1950–1970 period to 0.99 over 1980–2000. This indicates that housing
supply in the United States became more inelastic, or less price responsive, over
this period. The third column reports the standard deviation (across MSAs) by
year for the same supply statistic. The MSAs had tightly clustered slopes of
their supply curves between 1950 and 1980. But by 1980 or so, the standard
deviation increased considerably, almost tripling. This pattern suggests that
a small tail of MSAs experienced large decreases in supply elasticity (higher
slope numbers), generating more dispersion.
These changes in the elasticity of supply over time present a hurdle for
empirical researchers working at the intersection of real estate and urban economics because it means that the extent to which urban demand is capitalized
into land prices is not stable. Even if a researcher collects data on both land
prices and new construction, the changing ratio of price growth to a new supply
response to an underlying change in demand makes the evidence difficult to
interpret.
A further complication arises from an important issue pointed out in
Glaeser and Gyourko (2005): The elasticity of supply in a market depends
on the relationship between real estate prices and construction costs. The reason is that new supply is constructed only when real estate prices are in excess
of their cost of construction, so developers can make a profit. When prices are
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r
S’’
F
A
C
D’’
D’
D
Q
FIGURE 2: Supply and Demand for Real Estate with a Kinked Supply Curve.
below construction costs—perhaps because of prior overbuilding that shifted
the supply curve out, or because of a decline in demand relative to previous levels that shifted the demand curve in—supply is inelastic. Because real estate
is long-lived, if demand falls supply cannot contract in the short run, leading
to full price capitalization of the drop in demand. If demand rises, but not so
much that prices exceed construction costs, it still is not worthwhile for new
development to occur, and thus there again is full price capitalization. Glaeser
and Gyourko argue that, because of the kink in the supply curve induced by
construction costs, real estate supply is much more inelastic when prices are
not high enough to justify new construction than when prices are above construction costs. This argument is illustrated in Figure 2. As in Figure 1, the
demand curve (D) is downward-sloping and we consider an outward shift to
D . Unlike in Figure 1, the supply curve, S , is kinked rather than linear. It is
vertical up to a point and then slopes upward. The kink point is where prices
exceed construction costs. The points A and C are the same as in Figure 1: In
the range where the supply curve is vertical, shifts in demand are fully capitalized into rent. But an equal-sized demand shock, from D to D , that crosses
the kink point is not fully capitalized into rent, since F–A is smaller than A–C.
Instead, some of the increase in demand is reflected in new construction, and
thus higher Q, since construction is now profitable.
Despite the ephemeral nature of the elasticity of supply, much empirical
work has assumed it to be either perfectly inelastic or perfectly elastic. The
conclusions one draws about the underlying demand for a location are more
tenuous when one recognizes that these extremes of supply elasticity or inelasticity are rarely reached. The most accessible avenue of research would be
to take into account cross-sectional differences across space in the elasticity
of supply of land. This could be done using the data in Saiz (2008) or Gyourko, Saiz, and Summers (2008). By way of example, Glaeser, Gyourko, and
Saiz (2008) assess whether the amplitude and duration of house price bubbles
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vary with cross-sectional differences in the elasticity of supply. While it would
be much more difficult to measure changes in the elasticity of supply within
metropolitan areas over time, it would be a worthwhile research agenda.
2. THE FEEDBACK BETWEEN ASSET PRICES AND URBAN DEMAND
One of the potentially more interesting areas of intersection between urban
economics and real estate is when real estate prices—which are driven by
demand for a location—themselves feed back and affect the characteristics
that make an area desirable. These are issues of the dynamic evolution of
cities and raise the question of how urban areas will fare in the future.
This phenomenon is especially salient now because only recently has land
supply become fairly inelastic in some cities. As described in GMS (2006, 2009),
in the 1950s, every MSA in the United States had easily developable land remaining. By 1980, several MSAs became “filled-up” in the sense that it became
difficult or significantly more expensive to develop further within an MSA, and
new construction dried up relative to price growth in those MSAs. That trend
was exacerbated in 1990 and 2000.
According to GMS (2006), at least one of the reasons for this dynamic was
the considerable growth in population and incomes that the United States experienced over the 1950–2000 time period combined with inherent tastes in the
populace for some MSAs relative to others. A growing income-weighted population meant aggregate demand was growing, too. Some elastically supplied
MSAs could accept all potential residents; however, those with inelastic supply
experienced price growth. Initially, all MSAs were elastically supplied. But as
some cities started to reach physical or regulatory capacity, they shifted to more
inelastic supply. In GMS (2006), this is manifested as a shift in an MSA from
experiencing high housing-unit growth and low price growth to experiencing
low housing-unit growth and high price growth.
It is instructive to look at the impact of this particular dynamic as it seems
to have important implications for the future of over-demanded and undersupplied cities. Table 2 presents a measure of what fraction of U.S. households
could reasonably afford to purchase houses in various MSAs, using as an affordability criteria that a household could pay three times its reported income.
Using decennial Census data, I constructed the national income distribution,
and then asked, for each MSA, at what percentile the median-valued home and
10th percentile-valued home would map to the national income distribution in
that year.5
For example, the top panel of Table 2 reports the 10 MSAs that were the
least expensive in 2000. These MSAs include cities in states such as Texas and
Oklahoma. In the leftmost panel, I report where the median value home in
5
Although much of the discussion in this paper centers around housing markets, I would
be remiss not to highlight that urban economics and commercial real estate overlap as well.
Companies use land just like residents do.
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1
2
3
4
5
6
1
2
3
4
5
6
7
8
9
10
MSA
1960
10
20
10
5
20
20
20
20
10
30
40
20
30
20
30
40
50
30
40
40
40
1950
10 least expensive MSAs. . .
Brownsville
15
Enid
25
Joplin
15
McAllen
15
Abilene
25
Beaumont
25
Johnstown
25
Pine Bluff
15
Shermon-Denison
20
Steubenville-Weirton
25
45
25
20
20
25
5 median MSAs. . .
Akron
Asheville
Athens
Auburn-Opelika
Bloomington
15 most expensive MSAs. . .
San Jose
65
San Francisco
65
Santa Cruz
45
Orange County
45
Santa Rosa
45
Ventura
45
55
55
40
55
40
40
30
25
25
25
25
10
20
15
10
20
20
15
20
20
25
1970
90
90
80
90
80
80
30
30
30
30
30
15
30
25
15
25
25
25
25
25
30
1980
95
100
95
95
90
90
30
30
30
30
30
15
15
15
15
20
20
20
20
20
20
1990
. . .Median-Value Home in the MSA
95
95
95
85
85
75
35
35
35
35
35
20
20
20
20
20
20
20
20
20
20
2000
25
25
20
20
20
15
20
10
10
10
15
10
15
10
10
15
15
10
10
10
15
1950
30
30
10
30
10
20
20
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1960
30
30
20
30
20
25
15
10
10
10
15
5
5
5
5
5
10
5
5
5
10
1970
60
60
60
60
60
60
15
10
10
10
15
5
10
5
5
10
10
10
5
5
10
1980
85
85
65
50
50
50
20
20
20
20
20
10
10
10
10
10
10
10
10
10
10
2000
Continued
80
80
65
80
55
80
10
10
10
10
10
5
5
5
5
5
5
5
5
5
5
1990
. . .10th Percentile-Value Home in the MSA
The Percentile of the National Income Distribution Where Family Income Is Above One-Third of the. . .
TABLE 2: Trends in the Affordability of Typical Homes, by MSA
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45
65
65
25
45
65
65
45
25
40
40
50
30
40
N/A
40
40
40
1960
40
40
55
30
40
55
55
40
40
1970
80
80
60
60
80
60
60
60
60
1980
80
90
90
90
80
90
80
65
55
1990
85
85
75
75
75
75
75
75
75
2000
20
25
25
15
25
25
25
20
15
1950
10
20
40
10
30
N/A
30
20
10
1960
25
25
30
15
25
30
25
25
20
1970
60
30
30
30
60
25
25
30
60
1980
55
55
65
55
55
55
55
30
30
1990
. . .10th Percentile-Value Home in the MSA
45
45
45
45
45
45
45
45
45
2000
Notes: Data are from the U.S. Decennial Censuses, 1950–2000. Median and 10th percentile house values at the MSA level are compared to the
income distribution at the national level. The number reported in a cell is the percentile of the national income distribution in a given year that
is mapped to by the median (or 10th percentile) house value in the corresponding MSA in the given year. The rule for mapping is that the income
percentile is where the national income is one-third of the MSA’s house value. The “N/A” for New York occurs because of data limitations in the
Census in 1960.
1950
7
8
9
10
11
12
13
14
15
Salinas
Oakland
Bergen-Passic
San Luis Obispo
San Diego
New York
Nassau-Suffolk County
Seattle
Boulder
MSA
. . .Median-Value Home in the MSA
The Percentile of the National Income Distribution Where Family Income Is Above One-Third of the. . .
TABLE 2: Continued
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those 10 MSAs fall in the national income distribution for each decade from
1950 to 2000. The percentiles range from 5 to 30, with most being around
20 percent. That means a median-valued house in each of these MSAs could
have been purchased for three times annual income by anywhere from 70 to
95 percent of U.S. households in that year, which is an indication that these
MSAs were inexpensive relative to the country as a whole. The left panel of the
top of Table 2 shows little change over time, as well.
A similar pattern is repeated for the rightmost panel of the top of Table 2,
which reports where the 10th percentile (by value) houses in the same 10
MSAs fall in the national income distribution. Such houses in these cities were
affordable by 85 to 95 percent of U.S. households from 1950 to 2000 and that
degree of affordability changed little over this time period. Clearly, for this set
of MSAs, house prices did not rise relative to the national income distribution.
The middle panel of Table 2 reports where the median (left panel) and 10th
percentile (right panel) by value houses fall in the national income distribution
for five MSAs at the middle of the house price distribution. Median houses
in these MSAs were affordable to 60 to 80 percent of the national income
distribution and a 10th percentile house could be afforded by 80 to 95 percent
of households nationally, with little change over the 50-year sample period.
A quite different pattern can be found in the bottom panel of Table 2. The
MSAs in this panel are the 15 most expensive out of the 316 in the data. These
MSAs, which include much of coastal California, the New York area, Seattle,
and Boulder, seem to have started out as fairly typically priced. In the left
panel, the median houses in these cities in 1950 were affordable by around
half of U.S. families. (The actual points in the distribution range from the 25th
to 65th percentiles.) That degree of affordability persisted through the 1970s.
But starting around 1980, median house prices in these cities rose to higher
points in the national income distribution, ranging from the 60th to the 90th
percentile. By 2000, no more than 25 percent of the U.S. population could buy
the median house for three times income in these MSAs and in Santa Cruz,
San Francisco, and San Jose, just 5 percent of the U.S. population could.
The pattern repeats itself for the 10th percentile homes in these MSAs.
Between 1950 and 1970, the 10th percentile homes could be purchased by
the 10th to 30th percentile household in the United States. But starting in
1980, the range increased to the 25th to 60th percentile of households and, by
2000, the 10th percentile home in these MSAs could be purchased by just 15
to 55 percent of U.S. households. This change in affordability is matched by a
change in the resident population: GMS (2006) finds that cities such as these
experience decreases in their number of low- and middle-income residents and
increases in their number of high-income residents.6
6
One should not take the exact numbers in this exercise too literally. First, rather than
comparing the price of a constant unit of land across MSAs, Table 2 compares the value of a house
across MSAs. House value reflects land and structure, and quantity as well as price. The size and
quality of the housing structure, or the quantity of land, reflected in the median and 10th percentile
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The reason that this pattern is especially interesting is grounded in why
it is that some cities are more desirable to certain segments of the population
in the first place. GMS (2006) is agnostic about those reasons, but what if they
are influenced by the level of prices? Does the desirability persist when (say)
only 5 percent of the U.S. population can afford the median house and only 15
percent of the U.S. population can afford the 10th percentile house, as was the
case in San Francisco?
It is not a priori clear whether the screening on income or wealth that high
house prices imply is a good thing or a bad thing for cities. Perhaps if highincome households prefer to be around other high-income households, the market price mechanism reinforces the agglomeration benefits. By pricing those
who would not contribute to or benefit from the high-income agglomeration out
of a city, a high clearing price could prevent dilution of the concentration of
high income households. But what if the vibrancy of the city depends in part
on heterogeneity in the population, such as in Jacobs (1969), especially heterogeneity that is correlated with differences in income? What if artists, or young
chefs, or even the middle class help make cities attractive? One could imagine
that an equilibrium might exist where prices merely would fall just enough to
enable some of these beneficial households to live in the city, or perhaps such
households would command higher wages in that city. Alternatively, one could
surmise that if households that make a positive contribution also prefer to agglomerate amongst themselves, displacing a cluster of such residents out of a
city could persist for the long-term or permanently since it would be difficult for
such an agglomeration to rebuild. Or, a city could cycle between homogeneity
and heterogeneity if moving is costly or slow to adjust. Lastly, if the positive
externalities created by households who contribute to heterogeneity—or the
negative externalities from those who displace them—are not internalized, it
is not clear what equilibrium might arise.
For example, many explanations of urban growth and success depend on
agglomeration externalities—positive spillovers between closely located firms
or households. Those externalities can be productive, in that firms can produce
output more efficiently if agglomerated, or consumption-based, in that households enjoy a location more if surrounded by positive externality-producing
households. In these cases, the value of a location is not necessarily inherent
in the physical geography, but arises from who is there.
Take, for example, the consumption benefits of living in cities (Glaeser,
Kolko, and Saiz, 2001). City amenities, such as restaurants, theaters, or the
social scene, arguably make a city more attractive to potential residents and
thus more valuable. But as Waldfogel has argued in a compelling series of
papers (see, e.g., Waldfogel, 2003), what amenities are provided in a city, and
at what level of quality, depends upon the composition of the residents. When
residents share similar tastes, amenities (or products) arise endogenously to
values might vary across MSAs. Second, the Census reports house values and incomes in bins, so
the percentiles are measured with noise.
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serve the local market. Given fixed costs of producing such amenities, goods, or
services for the local market, there needs to be a sufficiently large audience to
make production worthwhile. In addition, the quality and variety of products
provided increase in the size of the market. In Waldfogel’s framework, each
resident potentially provides a positive externality to other residents who share
similar preferences.
When a city enjoys elastic land supply, the return to the consumption agglomeration is not capitalized into land prices. Instead, the residents enjoy
more surplus or perhaps firms can pay lower wages. But when a city is inelastically supplied, land prices will capitalize the returns to scale. As long as
all the residents produce and enjoy the consumption externalities symmetrically, presumably house prices will rise just enough that the marginal resident
is indifferent between moving in and enjoying the consumption amenities, or
not.
But what if some residents benefit more from the consumption amenities
and others produce them? In that case the beneficiaries of the externalities
would have a higher willingness to pay for an area than the producers. When
land is elastically supplied, this asymmetry does not matter since both types of
households could purchase property at the cost of construction. But when land
is inelastically supplied, the existence of a consumption agglomeration could
drive prices high enough that only beneficiaries of the agglomeration would
be willing to pay the premium to move into the city and externality producers
would not. In the short run, this would not affect the existing agglomeration since the existing land owners would enjoy an increase in wealth—from
the rise in prices—that would just enable them to remain in the city even if
they were producers of the externality (Ortalo-Magné and Rady, 2008). But,
over time, is the consumption agglomeration sustainable absent government
intervention?7
While low-income service providers, such as teachers, policemen, or gardeners, can command higher wages in a high-priced city, that occurs because
their services are paid for by those who receive the benefits of them. However,
when some members of the population, by their mere presence, create a public
good, it is difficult to generate appropriately higher wages for them. Due to the
free rider problem, beneficiaries of the public good are unlikely to voluntarily
pay high enough wages to lead to the optimal provision of the public good.
Instead, government intervention may be necessary to subsidize the presence
of public good providers. Some existing local public policies affect the composition of residents, presumably to manage externalities. Exclusionary zoning,
where potential entrants into a community are barred from purchasing small
plots of land, impose a minimum wealth threshold for residence since land consumption is constrained to be high (Inman and Rubinfeld, 1979). On the other
7
One could draw analogies to various strands of the Tiebout literature—see Scotchmer
(2002) for a summary of club models in particular. It seems likely that at best the agglomeration
would be inefficiently sized and at worst equilibrium may not exist.
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437
end of the spectrum, subsidized housing may be provided to enable low-income
residents to live in a high-priced area. Another example is the practice of local
governments giving incentives for firms to locate facilities in particular areas.
Of course, the difficulty with explicitly subsidizing perceived contributors to
the public good is that the government would need to be able to identify such
residents and place a value on their contributions.
Not all MSAs have ended up so high-priced as to crowd out potential residents. The top panel of Table 3 reports the mapping from the median and 10th
percentile house values to the national income distribution for the 10 MSAs
that experienced the largest gains in affordability. The list comprises declining
cities: Cincinnati, Rochester, and Milwaukee are among the MSAs. In 1950,
the median houses in many of these MSAs were unaffordable to the median
household nationally, with the mapping into the percentile of the national income distribution reaching as much as the 65th percentile. But the exclusivity
steadily declined over the next 50 years, so by 2000, their percentile in the
national income distribution dropped by anywhere from 30 to 15 percentage
points. (Less, if any, decline was seen for the 10th percentile homes in these
MSAs.) In another example of how the land prices can feed back into how an
urban area develops, Glaeser and Gyourko (2005) have argued that declining
cities such as these—ones with existing stocks of housing and thus inelastic
supply—can become magnets for low-income, low ability families whose collective presence deters higher-income families from locating there since the
higher-income families would face an excessive local tax burden. Such highincome families can afford to live in other MSAs, while low-income families
remain because house prices are below the cost of new construction in growing
cities.8
The bottom panel of Table 3 shows a completely different set of cities, those
that experienced the greatest increase in where their house values mapped into
the national income distribution. Most of these MSAs span the west coast of
the United States and experienced an increase in anywhere from 25 to 50
percentiles for their median valued houses and 15 to 60 percentiles for their
10th percentile valued houses.
Despite the focus on the MSAs at the extremes, most metropolitan areas
did not experience much, if any, change in their affordability over the 1950 to
2000 time period. The middle panel of Table 3, which reports the mapping from
the median and 10th percentile house values to the national income distribution
for five MSAs around the median change in affordability, reflects this. Growth
in the median and 10th percentile house prices in these median MSAs tracked
growth in the median and 10th percentile incomes at the national level over
the entire 1950 to 2000 sample period.
8
Alternatively, urban living may lead to greater development of cognitive skills (Bacolod
et al., 2010).
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1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
9
10
MSA
40
30
40
25
25
30
25
25
40
40
30
25
20
20
25
40
30
40
40
40
55
25
55
55
10 MSAs with the largest decline. . .
Cleveland
65
40
Cincinnati
65
40
Rochester
65
40
Elmira
45
30
El Paso
45
30
Binghamton
45
40
Waterloo-Cedar Falls
45
30
Utica-Rome
45
30
Milwaukee
65
40
Madison
65
40
5 MSAs with the median growth. . .
Corvallis
45
40
Eau Claire
25
20
Evansville
25
20
Fayetteville
25
20
Florence
25
20
15 MSAs with the largest rise. . .
Santa Cruz
45
San Luis Obispo
25
Boulder
25
Santa Rosa
45
Salinas
45
Orange County
45
Santa Fe
25
San Jose
65
San Francisco
65
30
30
40
40
40
40
30
40
50
1970
1960
1950
80
60
60
80
80
90
60
90
90
60
30
30
30
30
60
30
60
25
30
30
30
25
60
60
1980
95
90
55
90
80
95
55
95
100
30
25
25
25
25
30
30
30
25
25
45
20
30
45
45
1990
. . .Median Value Home in the MSA
95
75
75
85
85
85
65
95
95
50
30
30
30
30
35
35
35
20
20
25
25
25
45
45
2000
20
15
15
20
20
20
10
25
25
15
15
15
10
10
25
20
20
20
15
20
15
15
25
20
1950
10
10
10
10
10
30
5
30
30
10
5
5
5
5
20
10
20
10
10
10
10
10
30
20
1960
20
15
20
20
25
30
15
30
30
15
10
10
10
10
25
15
20
15
15
15
15
15
25
20
1970
60
30
60
60
60
60
25
60
60
30
15
10
10
10
25
15
25
10
10
15
15
10
25
30
1980
65
45
45
50
45
50
35
85
85
35
20
15
20
15
20
20
25
10
15
15
10
15
20
30
2000
Continued
65
55
30
55
55
80
30
80
80
20
10
10
10
10
15
15
20
10
10
20
10
10
20
25
1990
. . .10th Percentile-Value Home in the MSA
The Percentile of the National Income Distribution Where Family Income Is Above One-Third of the. . .
TABLE 3: Trends in the Affordability of Typical Homes, for MSAs with the Largest Changes over Time
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45
45
45
20
25
25
40
40
40
10
20
30
1960
40
40
40
25
25
25
1970
80
60
80
30
60
60
1980
90
65
80
30
45
30
1990
75
75
75
45
50
50
2000
15
20
25
10
15
15
1950
20
20
30
5
5
10
1960
25
25
25
5
15
15
1970
60
30
60
10
25
25
1980
80
30
55
10
25
20
1990
. . .10th Percentile-Value Home in the MSA
50
45
45
25
30
35
2000
Notes: Data are from the U.S. Decennial Censuses, 1950–2000. Median and 10th percentile house values at the MSA level are compared to the
income distribution at the national level. The number reported in a cell is the percentile of the national income distribution in a given year that
is mapped to by the median (or 10th percentile) house value in the corresponding MSA in the given year. The rule for mapping is that the income
percentile is where the national income is one-third of the MSA’s house value.
1950
10
11
12
13
14
15
Ventura
Seattle
San Diego
Wilmington
Bellingham
Portland
MSA
. . .Median Value Home in the MSA
The Percentile of the National Income Distribution Where Family Income Is Above One-Third of the. . .
TABLE 3: Continued
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3. BEING AN OWNER AS WELL AS A RESIDENT
Another feedback mechanism between real estate and urban economics
that has grown in importance as more areas have become inelastically supplied
is that the political decision-makers are typically also property owners. When
land supply was elastic, decisions about what amenities to provide or land
use regulations to impose would not affect residents’ wealth because property
values would be unchanged. But when such decisions are capitalized into land
values, as is the case when land supply is inelastic, property owners have a
financial interest in the outcome even if they would not directly benefit from
it. This point combines the gist of Fischel’s (2001) home-voter hypothesis with
a distinction between inelastic and elastically supplied areas.
For example, Hilber and Mayer (2009) show that in towns with inelastic
land supply, families without children in schools are more likely to vote for
increased school spending than similar families in towns where developable
land is plentiful. The ostensible reason is that higher school quality raises
property values in towns where it is hard to add to the housing stock because
the higher demand for living there leads to higher rental values. Families in
such a town not only get better schools when school spending increases, they
get capital appreciation in their real estate. In towns where it is easy to build
more housing, better quality schools do not lead to higher property values.
Instead, they lead to more real estate development. A family without children
in such a town would be less likely to support higher school spending since none
of the benefit of better schools would accrue to them through housing capital
gains.9
The effect outlined in Hilber and Mayer arises because the households
making the decision over what local amenity to provide are also the property
owners. When supply is inelastic, their wealth depends on the desirability of
their locality. Because of this relationship between how vested in the outcome a
household is and the elasticity of supply, it is natural that the very places where
land prices capitalize a greater fraction of the value of an amenity, amenities
are more likely to be provided and in greater value.
The recent downturn in the housing market adds a complication to the relationship between home ownership and the provision of amenities. When house
prices have fallen enough that home owners owe more on their mortgages than
their houses are worth, providing local amenities or public services that raise
house prices creates wealth for the lender rather than the homeowner. In that
case, a home owner has less of an incentive to support the provision of such
amenities (Ferreira, Gyourko, and Tracy, 2008). This issue is especially salient
when house price declines are pervasive throughout an entire jurisdiction because then the median voter may switch from a positive to negative equity
position.
9
This point is related to the literature on Tiebout sorting with public goods and voting. See
Epple and Nechyba (2004) for a synopsis.
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These issues provide interesting food for thought: How might the evolution of urban areas be different as the set of stakeholders changes? Does that
evolution accelerate or slow down depending on whether home owners have
positive equity in their homes?
In addition, this relationship between property ownership and amenity
provision leads to an empirical difficulty with using land prices to measure
outcomes. The very features of urban life that one wishes to value using land
rent capitalization are themselves influenced by whether their creation leads
to higher land prices.
Another example: In most urban research, appealing (or unappealing) features of an area are assumed to be exogenous, or the outcome of some process
that has little to do with the supply of land. It is interesting to consider then
what happens if this assumption is relaxed. Perhaps cities with limited land
are more likely to invest in parks and green space (thus further exacerbating
their supply inelasticity!) because the amenity raises their residents’ property
values or because it would increase the tax base. Perhaps a business improvement district, which is when a group of commercial property owners collectively
agree to tax themselves and use the proceeds to provide a set of common services, would be most successful when supply is inelastic because commercial
property owners would have a greater incentive to participate when their financial contributions to the B.I.D. would be offset by the subsequent increase
in their property values.
The fact that a homeowner’s wealth, through their own house values,
moves in lockstep with housing prices has proven to be powerful in other
contexts. Ortalo-Magné and Rady (2008) use it to explain how households of
divergent incomes can afford to live in the same (expensive) city: If the lowincome households purchased their houses earlier, when housing was cheap,
their wealth increased enough when their house prices rose to enable them
to remain residents. Thus old-timer low-income households coexist with newcomer high-income households. Sinai and Souleles (2005) use it to explain why
owning a house may reduce the risk of obtaining housing services. OrtaloMagné and Prat (2007) contend that, in part, a desire to maximize one’s own
wealth leads homeowners to vote for regulations that limit new housing construction since the resulting inelastic supply could lead to higher house prices
for them.
4. THE ROLE OF ASSET MARKET EQUILIBRIUM
The difficulty with—and the interesting thing about—prices is that they
capitalize more than just the current spot rent. While rent represents the outcome of a spatial equilibrium, getting from rents to prices involves asset market
equilibrium. That is, an investment in real estate should yield an equivalent
risk-adjusted return to an investment in some other asset. Because of this
additional equilibrium, the price of real estate incorporates not only the underlying demand for that location, reflected in the current rent, but also the
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factors that determine the willingness of an owner to pay for a future stream
of rental value. Those factors include interest rates, how rents might change in
the future, future prices, and uncertainty.
An example of a simple pricing rule for real estate that satisfies asset
price equilibrium is the price of a particular piece of real estate is equal to the
expected present value of its rental value plus the expected present value of
the sale price, less an adjustment for risk (Meese and Wallace, 1994; Sinai and
Souleles, 2005). For the sake of simplicity, this example leaves out complications
such as any option value inherent in the investment or the possibility that
prices could deviate from their fundamental values. This zero-NPV equilibrium
pricing rule can be written as:
T
T
rt
␲t
PT
T
(1)
+
,
−
P1 = E
t
t −
T
T
(1 + it )
(1 + it )
(1 + iT )
(1 + it )
t=1
t=1
where T is the holding period and it is the time-varying discount rate. The first
term in equation (1) is the expected present value of the rental stream from
the real estate. The second term is the present value of the expected sale price,
PT . The last two terms in equation (1) are present-value adjustments for the
cost of the uncertainty surrounding future rents (␲ t ) and the future sale price
(T ), respectively.
From equation (1), one can see the difficulty in using real estate asset
prices to proxy for rent. Current prices depend not only on current rent, but
on expectations about future rents and prices, the path of interest rates (the
discount rate), and future risk premia. If the same underlying factors that cause
rent to change also cause those other variables to change, it becomes impossible
to infer rental values solely from prices. For example, one can imagine that
an economic boom in a city that led to higher rents might also increase (or,
arguably, decrease) uncertainty, leading to a simultaneous change in the rental
value and the risk premium. Both changes would be reflected in the asset price.
Or, irrational expectations of future prices that deviate from the present value
of the expected rental stream could induce differences between value measured
using prices versus rents.
If we assume all buyers and sellers of properties use the same pricing
model, and rule out deviations in price from fundamental value, equation (1)
reduces to:
∞
∞
rt
␲t
(2)
−
P1 = E
t
t,
(1 + it )
(1 + it )
t=1
t=1
because the price at any time t is just equal to the present value of the future
rents less a risk adjustment. Even with those assumptions, the asset price of
real estate depends on the expectations of future rents as well as the current
rent. If a shift in the demand curve led solely to a proportional change in the
current and all future rents, price would be a fine proxy for the theoretically
appropriate rental value. But if the expected growth rate in rent changed along
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443
with the current rent, the capitalization into price would be magnified. Whether
or not that induces mismeasurement depends on what the econometrician is
trying to capture. And if the rent change is temporary, the measured percent
change in price will depend on the proportion of the price that is due to current
rental value versus the future value. In high expected rent growth cities, that
portion is smaller than in low expected rent growth cities (Himmelberg, Mayer,
and Sinai, 2005).
Equation (2) can be simplified further by assuming time invariant discount rates, i, unchanging risk premia, ␲, and a constant growth rate of rent,
g. In that case, equation (2) reduces to the familiar static Gordon Growth
Model: P = r/(i − g). These are strong assumptions that we have little empirical evidence on. Campbell et al. (2006) and Glaeser and Gyourko (2006) have
produced some relevant details in the housing context, with the former showing that risk premia are not constant over time in national time series data
and the latter showing that the rent and interest rate processes are meanreverting.
Even the simple static Gordon Growth Model has important implications
for researchers on the boundary of urban and real estate economics. For one, it
emphasizes that changes in the growth rate of rents, g, may be even more important for real estate prices than changes in the level of rents, r. Since g can differ
across cities, the relationship between price and rent—Pk/rk = 1/(i − gk)—will
also vary across cities, k, as shown empirically for housing markets in Sinai and
Souleles (2005) and GMS (2006). Second, if one assumes that g (and even ␲,
from earlier) varies across cities in a way that is unobservable to the researcher
but do not change over time, it follows that inferences about differences in rental
value can be obtained from prices only by looking at within-market changes in
log price and discarding the cross-sectional across-city variation (or imposing
some theoretical structure on the relationship). This same point would hold for
any unobservable factor that affected the gap between prices and rents differentially across cities. For example, in most available data we do not actually
observe the unit price or rent of real estate. Rather, we observe the price per
unit times the quantity purchased, as in a house price or monthly rent paid.
Since the sample of properties over which prices are observed might be quite
different than the sample of properties over which rent is observed, and those
samples can vary across cities, the comparability of prices to rents might differ
across cities. For these reasons, most researchers restrict their attention to
within-city changes in prices, rents, or the ratio, though others try to make the
measures more comparable (Smith and Smith, 2006).
Overall, the Gordon Growth relationship needs to be true on average if
researchers wish to proxy for within-city changes in rent using changes in real
estate prices without including additional controls. In particular, it implies
that the growth rate of prices should track the growth rate of rents since
ln(Pt ) − ln(Pt−1 ) = ln(rt ) − ln(rt−1 ) in each metropolitan area if g is constant
within MSA. To check this, in Figure 3 I have plotted the annual average growth
rate in real house/apartment rents and real house prices for 316 Metropolitan
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.03
.02
.01
0
Annual average real house price growth
1950-2000
.04
444
.005
.01
.015
.02
.025
Annual average real rent growth, 1950-2000
Predicted values
.03
45-degree line
Notes: Data are from the U.S. Decennial Censuses, 1950–2000. Each dot corresponds to an MSA
average growth rate over the 1950–2000 period.
FIGURE 3: The Relationship between Long-Run Rent and House
Price Growth.
Statistical Areas (MSAs) over the 1950–2000 period. The data come from the
decennial U.S. Census, which reports the distributions of rents paid by renters
and self-reported house values for owners at the county level in a number of
discrete bins. (The number and boundaries of the bins varies across Censuses.)
I aggregated the county level data in each Census year to the MSA level and,
assigning the midpoint value of each rent or price bin to any household in that
bin, computed the population-weighted average rent and price by MSA and
Census year. After deflating by the CPI, I calculated the geometric growth rate
over the 1950–2000 period.
Figure 3 shows that long-run growth rates in rent and prices are quite
correlated and track each other nearly one-for-one, with rent growth and price
growth tightly clustered around a straight line. The coefficient on the bivariate
regression line, which is the thick line in Figure 3, is 1.06 (with a standard
error of 0.05), approximately parallel to the 45-degree line (the thin line). This
implies that a one-percentage point higher long-run rent growth in an MSA
corresponds to an approximately one-percentage point higher long-run price
growth on average. Overall, the correlation between the two growth rates is
0.75 and the R-squared of the bivariate regression is 0.56.
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0
.02
.04
.06
445
-.02
Annual average real house price growth
20-year window
SINAI: FEEDBACK BETWEEN REAL ESTATE AND URBAN ECONOMICS
-.02
0
.02
.04
Annual average real rent growth, 20 year window
Predicted values
.06
45-degree line
Notes: Data are from the U.S. Decennial Censuses, 1950–2000. Each dot corresponds to an MSA
average growth rate during a 20-year period between 1950 and 2000, so there are four dots per
MSA.
FIGURE 4: The Relationship between 20-Year Rent and House Price
Growth, 1970–2000.
Although the slope of the regression line parallels the 45-degree line, the
intercept of the regression line is higher. For the low-rent growth portion of the
sample, long run price growth ends up being about double rent growth.
This relationship between price and rent growth holds less well over
shorter horizons, reflecting that there might be temporary deviations from
the average in the other parameters in equation (1). Figure 4 plots the 20-year
average growth rates in real rents and prices for each of the 316 MSAs, treating
as independent the 1950–1970, 1960–1980, 1970–1990, and 1980–2000 periods. The correlation between the 20-year growth rates in rents and prices is
0.52 and the R-squared of the bivariate regression is just 0.27. This result
is consistent with the finding in Gallin (2004) that the relationship between
rents and prices varies over time but is mean-reverting. Despite the greater
noise, on average 20-year price changes are again about the same as 20-year
rent changes. Although the estimated coefficient of 0.65 (0.03) is attenuated
from the 1.0 of the 45-degree line, within the range of most of the sample the
attenuation is offset by a higher intercept on the regression line. This pattern
can be seen in Figure 4 by comparing the thick (regression) line and the thin
(45-degree) line.
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JOURNAL OF REGIONAL SCIENCE, VOL. 50, NO. 1, 2010
5. CONCLUSION
Since 1950, the United States has experienced increasing inelasticity of
land supply in a subset of its metropolitan areas. The resulting greater capitalization of demand into land rents and prices emphasizes the importance of the
overlap between real estate and urban economics. Namely, how does demand
for urban areas affect real estate prices, and what can we learn from them?
And, how do rising real estate prices affect the evolution of urban areas?
This paper described a few stylized empirical facts and outlined some of
the potential causes and implications. One fact was that house prices in some
metropolitan areas have grown increasingly expensive, relative to the national
income distribution, over the last 50 years. In some California cities, by 2000 the
median valued house was affordable by just 5 percent of the national population
and the 10th percentile house by just 15 percent. By contrast, in a number
of other metropolitan areas, house prices became either no less unaffordable
or, in the case of some declining cities, more affordable to the bulk of the
U.S. population. Another fact was that, on average, house price growth was a
reasonable proxy for the growth in rental value. Not only was this a useful
confirmation of a key prediction of the asset market equilibrium concept, but it
suggested that, at least in a long-run setting, prices reasonably substitute for
rents in empirical work.
Interesting questions and challenges remain well beyond those discussed
in this paper. Fortunately for empirical researchers, the quantity and quality of data on real estate has improved tremendously. House price indices are
available for most metropolitan areas since 1980. Transactions data for individual houses, from sources such as DataQuick, are becoming more common.
Other providers have collected decades of data on rents for commercial real
estate. Advances in geographic information systems, discussed elsewhere in
this volume, enable matching of local characteristics to real estate data.
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